Robitzsch Alexander
IPN-Leibniz Institute for Science and Mathematics Education, D-24098 Kiel, Germany.
Centre for International Student Assessment (ZIB), D-24098 Kiel, Germany.
J Intell. 2020 Aug 14;8(3):30. doi: 10.3390/jintelligence8030030.
The last series of Raven's standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.
在最近的一系列论文中，对瑞文标准渐进矩阵最后一组（SPM-LS）测试的心理测量特性进行了研究。在本文中，使用正则化潜在类别模型（RLCMs）对SPM-LS数据集进行分析。对于二分法项目反应数据，提出了一种基于融合正则化的RLCMs替代估计方法。对于多分法项目反应，给出了不同的替代融合正则化惩罚。在模拟数据示例和SPM-LS数据集中证明了所提出方法的有效性。对于SPM-LS数据集，结果表明正则化潜在类别模型产生了五个部分有序的潜在类别。总共有五个潜在类别中的三个在所有项目上是有序的。对于其余两个类别，分别发现了两个和三个项目的违规情况，这可以解释为一种潜在的项目功能差异。
